Skip to main content
Springer Nature Link
Log in

Latent Class Analysis: An Alternative Perspective on Subgroup Analysis in Prevention and Treatment

  • Published:
Prevention Science Aims and scope Submit manuscript

Abstract

The overall goal of this study is to introduce latent class analysis (LCA) as an alternative approach to latent subgroup analysis. Traditionally, subgroup analysis aims to determine whether individuals respond differently to a treatment based on one or more measured characteristics. LCA provides a way to identify a small set of underlying subgroups characterized by multiple dimensions which could, in turn, be used to examine differential treatment effects. This approach can help to address methodological challenges that arise in subgroup analysis, including a high Type I error rate, low statistical power, and limitations in examining higher-order interactions. An empirical example draws on N = 1,900 adolescents from the National Longitudinal Survey of Adolescent Health. Six characteristics (household poverty, single-parent status, peer cigarette use, peer alcohol use, neighborhood unemployment, and neighborhood poverty) are used to identify five latent subgroups: Low Risk, Peer Risk, Economic Risk, Household & Peer Risk, and Multi-Contextual Risk. Two approaches for examining differential treatment effects are demonstrated using a simulated outcome: 1) a classify-analyze approach and, 2) a model-based approach based on a reparameterization of the LCA with covariates model. Such approaches can facilitate targeting future intervention resources to subgroups that promise to show the maximum treatment response.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  • Agrawal, A., Lynskey, M. T., Madden, P. A., Bucholz, K. K., & Heath, A. C. (2007). A latent class analysis of illicit drug abuse/dependence: Results from the National Epidemiological Survey on Alcohol and Related Conditions. Addiction, 102, 94–104.

    Article  PubMed  Google Scholar 

  • Agresti, A. (2002). Categorical data analysis (2nd ed.). New York: Wiley.

    Book  Google Scholar 

  • Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage.

    Google Scholar 

  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.

    Article  Google Scholar 

  • Anderson, K. G., Ramo, D. E., Cummins, K. M., & Brown, S. A. (2010). Alcohol and drug involvement after adolescent treatment and functioning during emerging adulthood. Drug and Alcohol Dependence, 107, 171–181.

    Article  PubMed  Google Scholar 

  • Arthur, M. W., Hawkins, J. D., Pollard, J., Catalano, R. F., & Baglioni, A. J. (2002). Measuring risk and protective factors for substance use, delinquency, and other adolescent problem behaviors: The Communities that Care Youth Survey. Evaluation Review, 26, 575–601.

    PubMed  Google Scholar 

  • Baucom, B. R., Atkins, D. C., Simpson, L. E., & Christensen, A. (2009). Prediction of response to treatment in a randomized clinical trial of couple therapy: A 2-year follow-up. Journal of Consulting and Clinical Psychology, 77, 160–173.

    Article  PubMed  Google Scholar 

  • Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9, 291–319.

    Article  PubMed  CAS  Google Scholar 

  • Bozdogan, H. (1987). Model selection and Akaike’s Information Criterion (AIC): The general theory and its analytical extensions. Psychometrika, 52, 345–370.

    Article  Google Scholar 

  • Brooks-Gunn, J., Duncan, G. J., & Maritato, N. (1997). Poor families, poor outcomes: The well-being of children and youth. In G. J. Duncan & J. Brooks-Gunn (Eds.), Consequences of growing up poor (pp. 1–17). New York: Russell Sage.

    Google Scholar 

  • Catalano, R. F., & Hawkins, J. D. (1996). The social development model: A theory of anti-social behavior. In J. D. Hawkins (Ed.), Delinquency and crime: Current theories (pp. 149–197). New York: Cambridge University Press.

    Google Scholar 

  • Chassin, L., Pitts, S. C., & Prost, J. (2002). Binge drinking trajectories from adolescence to emerging adulthood in a high-risk sample: Predictors and substance abuse outcomes. Journal of Consulting and Clinical Psychology, 70, 67–78.

    Article  PubMed  Google Scholar 

  • Chung, T., Maisto, S. A., Cornelius, J. R., & Marti, C. S. (2004). Adolescents’ alcohol and drug use trajectories in the year following treatment. Journal of Studies on Alcohol, 65, 105–114.

    PubMed  Google Scholar 

  • Cicchetti, D., & Rogosch, F. A. (1996). Equifinality and multifinality in developmental psychopathology. Development and Psychopathology, 8, 597–600.

    Article  Google Scholar 

  • Coffman, D. L., Patrick, M. E., Palen, L. A., Rhoades, B. L., & Ventura, A. K. (2007). Why do high school seniors drink? Implications for a targeted approach to intervention. Prevention Science, 8, 241–248.

    Article  PubMed  Google Scholar 

  • Coie, J. D., Watt, N. F., West, S. G., Hawkins, J. D., Asarnow, J. R., Markman, H. J., et al. (1993). The science of prevention: A conceptual framework and some directions for a national research program. American Psychologist, 48, 1013–1022.

    Article  PubMed  CAS  Google Scholar 

  • Colder, C. R., Campbell, R. T., Ruel, E., Richardson, J. L., & Flay, B. R. (2002). A finite mixture model of growth trajectories of adolescent alcohol use: Predictors and consequences. Journal of Consulting and Clinical Psychology, 70, 976–985.

    Article  PubMed  Google Scholar 

  • Collins, L. M., & Lanza, S. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. New York: Wiley.

    Google Scholar 

  • Collins, L. M., Fidler, P. L., Wugalter, S. E., & Long, J. D. (1993). Goodness-of-fit testing for latent class models. Multivariate Behavioral Research, 28, 375–389.

    Article  Google Scholar 

  • Collins, L. M., Graham, J. W., Long, J. D., & Hansen, W. B. (1994). Crossvalidation of latent class models of early substance use onset. Multivariate Behavioral Research, 29, 165–183.

    Article  Google Scholar 

  • Collins, L. M., Murphy, S. A., & Bierman, K. (2004). A conceptual framework for adaptive preventive interventions. Prevention Science, 3, 185–196.

    Article  Google Scholar 

  • Conduct Problems Prevention Research Group. (1992). A developmental and clinical model for the prevention of conduct disorders: The FAST Track Program. Development and Psychopathology, 4, 509–527.

    Article  Google Scholar 

  • Dayton, C. M., & Macready, G. B. (1988). Concomitant variable latent class models. Journal of the American Statistical Association, 83, 173–178.

    Article  Google Scholar 

  • Elkin, I., Gibbons, R. D., Shea, M. T., Sotsky, S. M., Watkins, J. T., Pilkonis, P. A., et al. (1995). Initial severity and differential treatment outcome in the National Institute of Mental Health Treatment of Depression Collaborative Research Program. Journal of Consulting and Clinical Psychology, 63, 841–847.

    Article  PubMed  CAS  Google Scholar 

  • Everitt, B. S., & Hand, D. J. (1981). Finite mixture distributions. London: Chapman and Hall.

    Book  Google Scholar 

  • Fairchild, A. J., & MacKinnon, D. P. (2009). A general model for testing mediation and moderation effects. Prevention Science, 10, 87–99.

    Article  PubMed  Google Scholar 

  • Gerard, J. M., & Buehler, C. (2004). Cumulative environmental risk and youth maladjustment: The role of youth attributes. Child Development, 75, 1832–1849.

    Article  PubMed  Google Scholar 

  • Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, 215–231.

    Article  Google Scholar 

  • Harris, K. M., Halpern, C. T., Whitsel, E., Hussey, J., Tabor, J., Entzel, P., & Udry, J. R. (2009). The National Longitudinal Study of Adolescent Health: Research Design [WWW document]. URL: http://www.cpc.unc.edu/projects/addhealth/design.

  • Hawkins, J. D., Catalano, R. F., & Miller, J. Y. (1992). Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychological Bulletin, 112, 64–105.

    Article  PubMed  CAS  Google Scholar 

  • Kessler, R. C., Chiu, W. T., Demler, O., & Walters, E. E. (2005). Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 617–627.

    Article  PubMed  Google Scholar 

  • Komro, K. A., Tobler, A. L., Maldonado-Molina, M. M., & Perry, C. L. (2010). Effects of alcohol use initiation patterns on high-risk behaviors among urban, low-income, young adolescents. Prevention Science, 11, 14–23.

    Article  PubMed  Google Scholar 

  • Langeheine, R., Pannekoek, J., & van de Pol, F. (1996). Bootstrapping goodness-of-fit measures in categorical data analysis. Sociological Methods & Research, 24, 492–516.

    Article  Google Scholar 

  • Lanza, S. T., Collins, L. M., Schafer, J. L., & Flaherty, B. P. (2005). Using data augmentation to obtain standard errors and conduct hypothesis tests in latent class and latent transition analysis. Psychological Methods, 10, 84–100.

    Article  PubMed  Google Scholar 

  • Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14, 671–694. PMCID: PMC2785099.

    Article  PubMed  Google Scholar 

  • Lanza, S. T., Lemmon, D. R., Dziak, J. J., Huang, L., Schafer, J. L., & Collins, L. M. (2010). PROC LCA & PROC LTA user’s guide version 1.2.5 beta. University Park, PA: The Methodology Center, Penn State.

    Google Scholar 

  • Lanza, S. T., Rhoades, B. L., Nix, R. L., Greenberg, M. T., & the Conduct Problems Prevention Research Group. (2010). Modeling the interplay of multilevel risk factors for future academic and behavior problems: A person-centered approach. Development and Psycholopathology, 22, 313–335.

    Article  Google Scholar 

  • Laska, M. N., Pasch, K. E., Lust, K., Story, M., & Ehlinger, E. (2009). Latent class analysis of lifestyle characteristics and health risk behaviors among college youth. Prevention Science, 10, 376–386.

    Article  PubMed  Google Scholar 

  • Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston, MA: Houghton Mifflin.

    Google Scholar 

  • Loeber, R. (1990). Development and risk factors of juvenile antisocial behavior and delinquency. Clinical Psychology Review, 10, 1–41.

    Article  Google Scholar 

  • Luthar, S. S. (1993). Annotation: Methodological and conceptual issues in research on childhood resilience. Journal of Child Psychology and Psychiatry & Allied Disciplines, 34, 441–453.

    Article  CAS  Google Scholar 

  • MacKinnon, D. P. (2009). Introduction to statistical mediation analysis. New York: Lawrence Erlbaum Associates.

    Google Scholar 

  • McLachlan, G., & Peel, D. (2000). Finite mixture models. New York: Wiley.

    Book  Google Scholar 

  • Muthén, L. K., & Muthén, B. O. (1998–2007). Mplus user’s guide (5th ed.). Los Angeles, CA: Authors.

  • Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14, 535–569.

    Article  Google Scholar 

  • Ondersma, S. J., Winhusen, T., Erickson, S. J., Stine, S. M., & Wang, Y. (2009). Motivation enahancement therapy with pregnant substance-abusing women: Does baseline motivation moderate efficacy? Drug and Alcohol Dependence, 101, 74–79.

    Article  PubMed  Google Scholar 

  • Oxford, M. L., Gilchrist, L. D., Morrison, D. M., Gillmore, M. R., Lohr, M. J., & Lewis, S. M. (2003). Alcohol use among adolescent mothers: Heterogeneity in growth curves, predictors, and outcomes of alcohol use over time. Prevention Science, 4, 15–26.

    Article  PubMed  Google Scholar 

  • Rutter, M. (1979). Protective factors in children’s responses to stress and disadvantage. In M. W. Kent & J. E. Rolf (Eds.), Primary prevention of psychopathology: Vol 3. Social competence in children (pp. 49–74). Hanover, NH: University of New England Press.

  • Sameroff, A. J., Seifer, R., Baldwin, C. P., & Baldwin, A. (1993). Stability of intelligence from preschool to adolescence: The influence of social and family risk factors. Child Development, 64, 80–97.

    Article  PubMed  CAS  Google Scholar 

  • Scheier, L. M., Abdallah, A. B., Inciardi, J. A., Copeland, J., & Cottler, L. B. (2008). Tri-city study of Ecstasy use problems: A latent class analysis. Drug and Alcohol Dependence, 98, 249–263.

    Article  PubMed  Google Scholar 

  • Schochet, P. Z. (2008). Technical methods report: Guidelines for multiple testing in impact evaluations (NCEE 2008-4018). Washington, DC: National Center for Education Evaluation and Regional Assistance, Institute of Education Sciences, U.S. Department of Education.

    Google Scholar 

  • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461–464.

    Article  Google Scholar 

  • Sclove, S. L. (1987). Application of model-selection criteria to some problems in multivariate analysis. Psychometrika, 52, 333–343.

    Article  Google Scholar 

  • Shin, S. H., Hong, H. G., & Hazen, A. L. (2010). Childhood sexual abuse and adolescent substance use: A latent class analysis. Drug and Alcohol Dependence, 109, 226–235.

    Article  PubMed  Google Scholar 

  • Syvertsen, A. K., Cleveland, M. J., Gayles, J. G., Tibbits, M. K., & Faulk, M. T. (2010). Profiles of protection from substance use among adolescents. Prevention Science, 11, 185–196.

    Article  PubMed  Google Scholar 

  • Titterington, D., Smith, A., & Makov, U. (1985). Statistical analysis of finite mixture distributions. Chichester, UK: Wiley.

    Google Scholar 

  • Van de Pol, F., Langeheine, R., & De Jong, W. (1996). User’s manual: A latent class program. The Netherlands: Voorburg.

    Google Scholar 

  • Vermunt, J. K., & Magidson, J. (2005). Latent GOLD 4.0 user’s guide. Belmont, MA: Statistical Innovations Inc.

    Google Scholar 

  • Von Eye, A., & Bergman, L. R. (2003). Research strategies in developmental psychopathology: Dimensional identity an the person-oriented approach. Development and Psychopathology, 15, 553–580.

    Google Scholar 

  • Wang, C.-P., Brown, C. H., & Bandeen-Roche, K. (2005). Residual diagnostics for growth mixture models: Examining the impact of a preventive intervention on multiple trajectories of aggressive behavior. Journal of the American Statistical Association, 100, 1054–1076.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Methodology Center, The Pennsylvania State University, 204 E. Calder Way Suite 400, State College, PA, 16801, USA

    Stephanie T. Lanza

  2. Prevention Research Center, The Pennsylvania State University, 206 Towers Building, University Park, PA, 16802, USA

    Brittany L. Rhoades

Authors
  1. Stephanie T. Lanza
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Brittany L. Rhoades
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Stephanie T. Lanza.

Additional information

This study was supported by Award Numbers P50-DA010075 and R03-DA023032 from the National Institute on Drug Abuse. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse or the National Institutes of Health. The authors wish to thank John J. Dziak for helpful advice on the simulation of an outcome variable. This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lanza, S.T., Rhoades, B.L. Latent Class Analysis: An Alternative Perspective on Subgroup Analysis in Prevention and Treatment. Prev Sci 14, 157–168 (2013). https://doi.org/10.1007/s11121-011-0201-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11121-011-0201-1

Keywords